The present invention relates generally to quality management systems, and more particularly, the invention relates to a method, system, and storage medium for providing a dynamic multi-dimensional commodity modeling process for implementation via a quality management system.
Manufacturing operations typically involve some degree of monitoring production quality performance and provide quality control capability by monitoring and analyzing quality data. While various software applications exist for facilitating these activities, they are generally limited to a fixed set of programs that analyze and monitor quality data across static product characteristics. For example, in a hardware manufacturing environment, such technology may provide the capability of monitoring power supplies, specific suppliers, a unique part number, etc. Any desire to change the analytics or product dimensions to be monitored results in the need for extensive hard-code changes to the computing application or query. Attempts to rewrite software queries that will measure atypical characteristics take time, such as a few hours to several days. Thus, the ability to change performance monitoring and control actions across multiple part dimensions or characteristics in near real-time is not feasible.
This problem becomes significant in large manufacturing operations where thousands, or tens of thousands, of component parts are utilized for production, especially when many of these parts have common characteristics (e.g., same supplier, same function, same size, etc.). In these operations, there exists a near daily need to analyze quality data in a variety of ways to understand part performance issues. The dynamic need to change analytics across multiple dimensions presents significant problems with existing processes and technology. This problem is most evident in manufacturing operations where complex products are produced in a “build-to-order” environment with a high degree of featurability. As operations move from mass production of like products to customized assemblies with a lot size of one, simple analytics such as failure rates are ineffective in characterizing performance.
What is needed, therefore, is a way to provide flexible, commodity data modeling that allows for analysis criteria to be alterable in a near real-time environment.